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Updated: May 30, 2026

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通过自适应参数学习平衡流体散射减弱对水下光学成像的影响

Jian Yang, Pengfei Han, Xuelong Li

|November 14, 2024

概括

本研究介绍了一种可学习的物理成像模型 (LPIM),用于对抗由流体散射和衰减引起的水下图像退化. 适应性框架改善了水下光学成像质量和视觉效果.

科学领域:

计算机视觉计算机视觉
光学成像技术的成像
流体动力学流体动力学

背景情况:

水下光学成像受到流体散射和衰减的阻碍.
现有的模型与多样化和动态的水下环境作斗争.

研究的目的:

开发适应性物理成像模型用于水下光学成像.
为了抵消由流体散射和衰减引起的图像退化.

主要方法:

提出了一个可学习的物理成像模型 (LPIM),具有对抗性竞争和定向学习.
开发了一个多分支网络 (R-Net,D-Net,BN-net) 来捕捉颜色,光强度和衰减.
学习了复杂的水下条件的非线性降解参数.

主要成果:

该LPIM有效地平衡流体散射减弱影响.
在色彩平衡和视觉效果方面,在最先进的方法上表现出卓越的性能.
通过对多个数据集的实验进行验证.

结论:

LPIM为水下图像恢复提供了一个创新的框架.
适应性方法在具有挑战性的水生环境中增强了光学成像.
该模型显著提高了水下图像的视觉质量和颜色保真度.

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